Automatic personality trait recognition has attracted increasing interest in psychology, neuropsychology, and computer science, etc. Motivated by the great success of deep learning methods in various tasks, a variety of deep neural networks have increasingly been employed to learn high-level feature representations for automatic personality trait recognition. This paper systematically presents a comprehensive survey on existing personality trait recognition methods from a computational perspective. Initially, we provide available personality trait data sets in the literature. Then, we review the principles and recent advances of typical deep learning techniques, including deep belief networks (DBNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Next, we describe the details of state-of-the-art personality trait recognition methods with specific focus on hand-crafted and deep learning-based feature extraction. These methods are analyzed and summarized in both single modality and multiple modalities, such as audio, visual, text, and physiological signals. Finally, we analyze the challenges and opportunities in this field and point out its future directions. The trait theory  and linear regressors, are usually used. In this survey, we focus on the advances of feature extraction algorithms ranging from 2012 to 2022 in a basic personality trait recognition system. In this work, our contributions can be summarized as follows:We provide an up-to-date literature survey on deep personality trait analysis from a perspective of both single modality and multiple modalities. In particular, this work focuses on a systematical single and multimodal analysis of human personality. To the best of our knowledge, this is the first attempt to present a comprehensive review covering both single and multimodal personality trait analysis related to hand-crafted and deep learning-based feature extraction algorithms in this field.We summarize existing personality trait data sets and review the typical deep learning techniques and its recent variants. We present the significant advances in single modality personality trait recognition related to audio, visual, text, etc., and multimodal personality trait recognition related to bimodal and trimodal modalities.We analyze and discuss the challenges and opportunities faced to personality trait recognition and point out future directions in this field.The remainder of this paper is organized as follows. Section “Personality Trait Databases” describes the available personality trait data sets. Several typical deep learning techniques and its recent variants are reviewed in detail in Section “Review of Deep Learning Techniques.” Section “Review of Single Modality Personality Trait Recognition Techniques” introduces the related techniques of single modality personality trait recognition. Section “Multimodal Fusion for Personality Trait Recognition” provides the details of multimodal fusion for personality trait recognition. Section “Challenges and Opportunities” discusses the challenges and opportunities in this field. Finally, the conclusions are given in Section “Conclusion.”To evaluate the performance of different methods, a variety of personality trait data sets, as shown in The SSPNet  speaker The Emergent LEAder ELEA;  data setThe SEMAINE  audio-viThe YouTube Vlogs  data setThe ChaLearn First Impression data set has been developed into two versions: the ChaLearn First Impression V1 , and theThe multimodal human–human–robot interactions MHHRI;  data setThe understanding dyadic interactions from video and audio signals UDIVA;  data setFrom In recent years, deep learning techniques have been an active research subject and obtained promising performance in various applications, such as object detection and classification, speech processing, natural language processing, and so on . In esseDeep belief networks DBNs;  developeSeveral improved versions of DBNs are developed in recent years. Convolutional neural networks (CNNs) were originally proposed by In recent years, several advanced versions of deep CNNs have been presented in various applications. The representative deep CNN models include AlexNet , VGGNet Compared with the above-mentioned deep CNNs processing 2D images, the recently developed 3D-CNNs  aim to lRecurrent neural networks RNNs;  are a siLong short-term memory LSTM; , proposeIn recent years, a variant of LSTMs called gated recurrent unit GRU; , was devAutomatic personality trait recognition aims to adopt computer science techniques to realize the modeling of personality trait recognition problems in cognitive science. It is one of the most important research subjects in the field of personality computing . AccordiThe early-used audio features for automatic personality trait recognition are hand-crafted low-level descriptive (LLD) features, such as prosody , voice quality (formants), spectral features , and so on. Specially, The recently used audio features for automatic personality trait recognition are deep audio features extracted by deep learning techniques. According to the type of vision-based input data, visual-based personality trait recognition can be categorized into two types: static images and dynamic video sequences. Visual feature extraction is the key step related to the input static images and dynamic video sequences for personality trait recognition. As far as static image-based personality trait recognition is concerned, researchers have found that a facial image presents most of meaningful descriptive cues for personality trait recognition . Hence, via fine-tuning a pretrained VGG-face model for feature learning so as to predict personality traits and intelligence jointly. They aimed to explore whether self-reported personality traits and intelligence can be jointly measured from facial images. In recent years, CNNs were also widely used for facial feature extraction on static image-based personality trait recognition tasks. Dynamic video sequences consist of a series of video image frames, thereby providing temporal information and scene dynamics. This brings about certain useful and complementary cues for personality trait analysis .In , the autIn consideration of the tremendous progress in the areas of deep learning, CNNs and LSTMs are widely for personality trait analysis from dynamic video sequences. In addition to the above-mentioned audio and visual modality, there are other single modalities, such as text, and physiological signals, etc., which can be applied for personality trait recognition. The text modality can effectively display traces of the user’s personality . One of via deep learning, which integrated semantic and emotional features from social media texts. They conducted sentence-level extraction of both semantic and emotion features by means of a BERT model and a SentiNet5 . As the simplest way of implementing feature integration, feature-level fusion has relatively low cost and complexity. Moreover, it considers the correlation between modalities. However, integrating different time scale and metric level of features from multimodal modalities will significantly increase the dimensionality of the concatenated feature vector, resulting in the difficulty of training models.In decision-level fusion, each modality is firstly modeled independently, and then these obtained results from single-modality are combined to produce final results by using a certain number of decision fusion rules. Decision-level fusion is thus called late fusion (LF). The commonly used decision fusion rules include “Majority vote,” “Max,” “Sum,” “Min,” “Average,” “Product,” etc. . Since dModel-level fusion aims to separately model each modality while taking into account the correlation between modalities. Therefore, it can consider the inter-correlation among different modalities and loose the demand of timing synchronization of these modalities.For bimodal modalities based personality trait recognition, the widely used one is audio-visual modality. In order to effectively extract audio-visual feature representations of short video sequences, numerical studies have been conducted for audio-visual personality trait recognition.Zhang et al., developed a deep bimodal regression (DBR) method so as to capture rich information from the audio and visual modality in videos . Figure via concatenation of their obtained latent features. For text modality, two language models, such as a skip-thought vector model and a bag-of-words model, were employed for text feature extraction. Finally, a concatenation of audio, visual, text-based latent features was implemented at feature-level for multimodal first-impression analysis.Escalante et al. explored the explainability in first impressions analysis from video sequences at the first time. They provided a baseline method of integrating audio, visual, and text (audio transcripts) information . They usn-gram CNN model, respectively. These extracted audio, visual, and text features were fed into a fully connected layer followed by a sigmoid function for the final personality trait prediction. It was concluded that the concatenation of audio, visual, and text features in feature-level fusion showed comparable performance with the averaging method in decision-level fusion.To date, although there are a number of literature related to multimodal personality trait prediction, showing its certain advance, a few challenges still exist in this area. In the following, we discuss these challenges and opportunities, and point out potential research directions in future.Although researchers have developed a variety of relevant data sets for personality trait recognition, as shown in In addition, owing to the difference of data collecting and annotating environment, data bias and inconsistent annotations usually exist among these different data sets. Most researchers conventionally verify the performance of their proposed methods within a specific data set, resulting in promising results. Such trained models based on intra-data set protocols commonly lack generalizability on unseen test data. Therefore, it is interesting to investigate the performance of multimodal personality trait recognition methods in cross-data set environment. To address this issue, deep domain adaption methods  may be aFor multimodal personality trait recognition, bimodal modalities like audio-visual, or trimodal modalities like audio, visual, and text, are usually employed. Note that the user’s physiological responses to affective stimuli are highly correlated with personality traits. However, few researchers explore the performance of integrating physiological signals with other modalities for multimodal personality trait recognition. This is because so far these are few multimodal personality trait recognition data sets, which incorporate physiological signals with other modalities. Hence, one may challenge is how to combine physiological signals and other modalities, such as audio, visual, and text clues, based on the corresponding developed multimodal data sets.Besides, other behavior signals, such as head and body pose information, which is related to personality trait clues , may preSo far, a variety of representative deep leaning methods have been successfully applied to learn high-level feature representations for automatic personality trait recognition. Moreover, these deep learning methods usually beat other methods adopting hand-crafted features. Nevertheless, these used deep learning techniques have a tremendous amount of network parameters, resulting in its large computation complexity. In this case, for real-time application sceneries it is often difficult to implement fast automatic personality trait prediction with these complicated deep models. To alleviate this issue, a deep model compression  may presAlthough deep learning has become a state-of-the-art technique in term of the performance measure on various feature learning tasks, the black box problem still exists. In particular, it is unknown that what exactly are various internal representations learned by multiple hidden layers of a deep model. Owing to its multilayer non-linear structure, deep learning techniques are usually criticized to be non-transparent, and their prediction results are often not traceable by human beings. To alleviate this problem, directly visualizing the learned features has become the widely used way of understanding deep models . Neverthvia the Big-Five personality model. This is because almost all of the current data sets were developed based on the Big-Five personality measures, as shown in It is noted that most researchers focus on personality trait analysis Due to the strong feature learning ability of deep learning, multiple recent works using deep learning have been developed for personality trait recognition associated with promising performance. This paper attempts to provide a comprehensive survey of existing personality trait recognition methods with specific focus on hand-crafted and deep learning-based feature extraction. These methods systematically review the topic from the single modality and multiple modalities. We also highlight numerous issues for future challenges and opportunities. Apparently, personality trait recognition is a very broad and multidisciplinary research issue. This survey only focuses on reviewing existing personality trait recognition methods from a computational perspective and does not take psychological studies into account on personality trait recognition.In future, it is interesting to explore the application of personality trait recognition techniques to personality-aware recommendation systems . In addiXZ contributed to the writing and drafted this article. ZT contributed to the collection and analysis of existing literature. SZ contributed to the conception and design of this work and revised this article. All authors contributed to the article and approved the submitted version.This work was supported by Zhejiang Provincial National Science Foundation of China and National Science Foundation of China (NSFC) under Grant Nos. LZ20F020002, LQ21F020002, and 61976149.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Recently, personality trait recognition, which aims to identify people’s first impression behavior data and analyze people’s psychological characteristics, has been an interesting and active topic in psychology, affective neuroscience and artificial intelligence. To effectively take advantage of spatio-temporal cues in audio-visual modalities, this paper proposes a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (CNN), bi-directional long short-term memory network (Bi-LSTM), and the Transformer network. In particular, a pre-trained deep audio CNN model is used to learn high-level segment-level audio features. A pre-trained deep face CNN model is leveraged to separately learn high-level frame-level global scene features and local face features from each frame in dynamic video sequences. Then, these extracted deep audio-visual features are fed into a Bi-LSTM and a Transformer network to individually capture long-term temporal dependency, thereby producing the final global audio and visual features for downstream tasks. Finally, a linear regression method is employed to conduct the single audio-based and visual-based personality trait recognition tasks, followed by a decision-level fusion strategy used for producing the final Big-Five personality scores and interview scores. Experimental results on the public ChaLearn First Impression-V2 personality dataset show the effectiveness of our method, outperforming other used methods. In personality psychology, researchers believe that human personality is innate, and have developed various theoretical methods to understand and measure a person’s personality. In recent years, researchers have employed computational techniques such as machine learning and deep learning methods  to modelIn a basic personality trait recognition system, two important steps are involved: feature extraction and personality trait classification or prediction . FeatureAccording to the types of extracted features characterizing personality traits, personality trait recognition techniques can be divided into hand-crafted based methods and deep learning based methods. Based on the extracted hand-crafted or deep learning features, previous works  focus onTo address these two issues above-mentioned, this paper proposes a multimodal personality trait recognition method integrating audio and visual modalities based on a hybrid deep learning framework. As depicted in The main contributions of this paper are summarized as follows:(1)This paper proposes a multimodal personality trait recognition method integrating audio and visual modalities based on a hybrid deep learning framework, in which CNN, Bi-LSTM, and Transformer are combined to capture high-level audio-visual spatio-temporal feature representations for personality trait recognition.(2)Extensive experiments are performed on the public ChaLearn First Impressions-V2 dataset and experimental results show that the proposed method outperforms other comparing methods on personality trait recognition tasks.The majority of prior works for personality trait recognition concentrates on single modality such as audio or visual cues, as described below.In early works, the conventional extracted hand-crafted audio features are low-level descriptor (LLD) features including intensity, pitch, formants, Mel-Frequency Cepstrum Coefficients (MFCCs), and so on. In recent years, researchers have tried to leverage deep learning  models wIn terms of the input type of visual data, visual-based personality trait recognition can be divided into two groups: static images-based and dynamic video sequences-based personality trait recognition.For static images-based personality trait recognition, the extracted visual features mainly come from facial features, since facial morphology provides explicit cues for personality trait recognition. In early works, the commonly used hand-crafted facial features are color histograms, local binary patterns (LBP), global descriptor, aesthetic features, etc. For dynamic video sequences-based personality trait recognition, dynamic video sequences contain temporal information related to facial activity statistics, thereby providing useful and complementary cues for personality trait recognition . In earlTo alleviate the problem of single modality based personality trait recognition, this paper proposes a multimodal personality trait recognition method integrating audio and visual modalities based on a hybrid deep learning framework. For audio signals in the video data, we use the pre-trained VGGish model  to extraFor visual signals in the video data, two preprocessing tasks are implemented. For global scene images in a video, 100 scene images are selected at equal intervals form each original video sample. Then, the resolution of each global scene image is resampled from the original 1280×720 pixels to 224×224 as inputs of VGG-Face model . For locAudio-visual feature extraction aims to learn the local and global feature representations from original audio and visual signals in a video for personality trait recognition, as described below.For the divided audio segment with 0.96 s, we leverage the VGGish model  pre-traiFor each scene and face image in a video, we employ the VGG-Face model  pre-traii-th input video clip ai  and its corresponding Big-Five personality score yi, we fine-tune the pre-trained VGGish network  represents the output of the last full connected layer in the VGGish network. θVG and VGW separately denotes the network parameters of the VGGish network and the weights of the sigmoid layer. The cross-entropy loss function L is defined as:where ηyj is the j-th ground-truth Big-Five personality score, and where For deep visual scene and face feature extraction on each frame of video, we fine-tune the pre-trained VGG-Face network  to learnAfter completing the local audio and visual feature extraction tasks, it is necessary to individually learn the global audio features, visual scene features, and visual face features from the entire videos so as to conduct personality trait prediction tasks. To this end, we adopt the Bi-LSTM  and receet, the learning process of the Bi-LSTM network is:Given an input sequence E ∈ ℝd1× is the learned temporal features, and WBi–LSTM is weight parameters of Bi-LSTM.where Q), key (K), and value (V), the output of each SDPA module is defined as:The original Transformer  is develdk is the feature dimension of the key matrix K.where After obtaining audio-visual global features extracted by a Bi-LSTM model and a Transformer model, we adopt a linear regression layer to predict the Big-Five personality and interview scores. The linear regression layer is calculated as follows:xi, wi, and b represent the i-th input sample, the corresponding weight value, and bias, respectively. fi(x) is the i-th prediction score value.where As shown in i is the weight value, fi(x) is the predicted value of each type of features, and where αY is the ground-truth score. Our goal is to minimize the MSE loss subject to where where λ is the Lagrange multiplier.m for m = 1,2,⋯6, as defined as:Then, we calculate the partial derivation of Eq. 8 based on αWe set the gradient to be 0, and get:1,α2,α3,α4,α5,α6]T = E[(fi(X)−Y)(fj(X)−Y)], Eq. 10 can be transformed as:Let α = Then, the optimal weight vector α can be obtained by:To verify the effectiveness of the proposed method, the public ChaLearn First Impression-V2  is emploe−4. After each epoch, the learning rate will become a half of the original learning rate. The maximum epoch number of is 30, and the Adam optimizer is used. The MSE loss function is adopted. The experimental platform is NVIDIA GPU Quadro M6000 with 24 GB memory. In order to improve the generalization performance of trained deep learning models and avoid overfitting, the early stopping strategy  with polynomial (poly), radial basis function (RBF), and linear kernel functions, Decision Tree Regression (DTR) are employed. In the SVR model, the degree of polynomial kernel function is 3, the penalty factor “The evaluation metric for evaluating the predicted personality trait or interview scores is defined as:N is the number of samples, yj is the ground-truth value. The higher the value S is, the better the obtained performance on personality or interview prediction tasks is.where In this section, two groups of experiments are carried out on the ChaLearn First Impression-V2 data set to verify the effectiveness of all used methods. One is the single-modal personality trait recognition, the other is multi-modal personality trait recognition.For single-modal personality recognition, we present the experiment results and analysis based on the single extracted audio features, visual scene features, and visual face features by using the pre-trained deep models.In summary, the results in For multimodal personality recognition tasks, we compare the performance of three typical multimodal information fusion methods, such as feature-level fusion, decision-level fusion, and model-level fusion. In feature-level fusion, the audio-visual global features learned by Bi-LSTM and Transformer networks, are concatenated into a whole feature vector as input of the linear regression layer for personality trait prediction. In this case, feature-level fusion is also called early fusion (EF). In model-level fusion (MF), the concatenated audio-visual global features are fed into a 4-layer full-collection layer network (1024-512-256-128) for personality trait prediction. In decision-level fusion, we adopt Eq. 12 to obtain the analytical solution of the optimal weight values in Eq. 6. In this case, decision-level fusion is also called late fusion (LF).To further verify the effectiveness of the proposed method, This paper presents a multimodal personality trait recognition method based on CNN + Bi-LSTM + Transformer network. In this work, CNN, Bi-LSTM, and Transformer are combined to capture high-level audio-visual spatio-temporal feature representations for personality trait recognition. Finally, we compare multimodal personality prediction results based on three different fusion methods such as feature-level fusion, model-level fusion, and decision-level fusion. Experiments on the public ChaLearn First Impression-V2 dataset show that decision-level fusion achieves the best multimodal personality trait recognition results with an average score of 0.9167, outperforming other existing methods.It is noted that this work only focuses on integrating audio and visual modalities for multimodal personality trait recognition. Considering the diversity of modal information related to the expression of personality traits, it is interesting to combine current audio-visual modalities with other modalities such as physiological signals, text cues, etc., to further improve the performance of personality trait recognition. In addition, exploring a more advanced deep learning model for personality trait recognition is also an important direction in our future work.https://chalearnlap.cvc.uab.cat/dataset/24/description/.Publicly available datasets were analyzed in this study. This data can be found here: XZ contributed to the writing and drafted the article. YL, ZT, YX, XT, DW, and GW contributed to the data preprocessing and analysis, software and experiment simulation. HL contributed to the project administration and writing—reviewing and editing. All authors contributed to the article and approved the submitted version.


First Person is a series of interviews with the first authors of a selection of papers published in Disease Models & Mechanisms, helping early-career researchers promote themselves alongside their papers. Stanley ‘Michii’ Kanai is first author on ‘ How would you explain the main findings of your paper to non-scientific family and friends?PLCB4 that produce mutated versions of the phospholipase C β4 (PLCB4) protein, although it was not clear if and how they cause ARCND2 in humans. PLCB4 normally acts as a relay that converts signals from outside the cell to a response inside the cell, but we show that this process is disrupted by mutant PLCB4 in a dominant-negative manner in cell culture experiments. Based on these results, we hypothesize that mutant PLCB4 is likely to prevent cells from forming correct facial bones and cartilages during embryonic development. Our experiments show that introducing a human PLCB4 gene variant into mice results in facial defects similar to those seen in individuals with ARCND2. In addition, we found a new bone containing tooth-like structures in the skull of Plcb4 mutant mice, and then compared with human anatomy to find these structures in the skull of an individual with ARCND2. The tooth-like structure resembles a bone present in the skull of reptiles, suggesting that disrupting PLCB4 can re-activate ancient programs governing facial development, resulting in facial birth defects.Craniofacial defects in newborns are one of the leading causes of infant mortality and can have life-long consequences for affected individuals. The overall goal of our research is to understand the causes of facial birth defects and to explore potential treatment strategies to prevent or mitigate these defects. In this study, we investigated auriculocondylar syndrome 2 (ARCND2), a rare disorder characterized by lower jaw defects, fusion of jaw joint bones and misshapen ear lobes (referred to as question mark ear). ARCND2 is associated with gene variations in What are the potential implications of these results for your field of research?“By establishing the role of a specific signal transduction pathway in ARCND2, we now have an opportunity to target this pathway, pharmacologically or otherwise, to prevent craniofacial anomalies.”These results lay the groundwork for investigating other puzzling aspects of ARCND2. For example, there is considerable phenotypic variation in individuals with ARCND2, both within family members and between families, suggesting that additional factors influence the severity of craniofacial malformations. By establishing PLCB4 variants as a causative factor for ARCND2, we and others can begin to explore whether environmental or genetic factors modify disease severity. These results may also have implications for personalized medicine. By establishing the role of a specific signal transduction pathway in ARCND2, we now have an opportunity to target this pathway, pharmacologically or otherwise, to prevent craniofacial anomalies. My hope is that our results will motivate future studies to determine the feasibility of this approach. Finally, our findings raise interesting questions about the plasticity of evolutionary adaptations; it would be exciting to see whether minor modifications in other signalling factors can also re-activate ancient developmental programs.What are the main advantages and drawbacks of the model system you have used as it relates to the disease you are investigating?PLCB4 locus. Although F0 animals are mosaic, our results indicate that the ARCND2 gene variants are pathogenic and provides motivation to invest additional time and resources to generate a stable mouse line for future studies. Overall, the tandem use of cell culture assays and an F0 mouse model offers a relatively rapid and rigorous approach towards establishing the genetic and cellular basis of a disease.We began our study using a mammalian cell culture model to characterize the functional consequence of PLCB4 variants. These results provided a strong rationale to extend our investigation into a mouse model. The main advantage of the mouse model is that the developmental processes and anatomy of the craniofacial skeleton in mice are highly conserved in humans. Therefore, craniofacial anomalies in mutant mice likely reflect the disease state in humans. The drawbacks, however, are that generating mouse models of disease is slower and more expensive relative to other model systems. We circumvented these issues by generating an F0 mouse model for ARCND2, using CRISPR/Cas9 to knock in a disease allele into the What has surprised you the most while conducting your research?I was surprised that all PLCB4 variants examined in this study behaved in a dominant-negative manner. Although our structural modelling predicted that the missense mutations in the catalytic pocket of PLCB4 would result in some kind of functional consequence, why these mutations cause dominant-negative interference rather than loss of function is not clear. I was also shocked to find that the ectopic bone structures in our mouse model were also present in human individuals with ARCND2. This was another reminder of the power of the mouse model for studying craniofacial development.Describe what you think is the most significant challenge impacting your research at this time and how will this be addressed over the next 10 years?in vitro and in vivo approaches. The second challenge is in understanding how disease variants, like those studied here, affect cell signalling pathways in vivo. Engineering genetically encoded signalling sensors that work in vertebrate model organisms is not trivial, as in vivo sensors often require greater selectivity and sensitivity than those used in cell culture models to account for the complexity of a living organism. Fortunately, many different sophisticated signalling sensors are being developed for in vitro use, with multiple groups also establishing reporter lines in zebrafish to detect acute signalling events such as calcium flux and MAP kinase activity. Over the next 10 years, I hope to build on these innovations and develop a research program that investigates the mechanism of human disease variants using in vitro assays, novel in vivo signalling reporters, and model organisms.I see two major challenges in our research. The first challenge is to understand the functional consequences of disease-associated gene variants. Advances in sequencing technology have made it possible to identify genetic variants associated with common and rare diseases at an unprecedented rate, although determining the functional significance and causal role of these variants is lagging. A coordinated effort between clinical geneticists and basic scientists is needed to accelerate the pace of variant validation using What changes do you think could improve the professional lives of early-career scientists?The academic research career-path is challenging, but having institutional support specifically for postdocs makes a big difference. For example, my institution, the University of Colorado Anschutz Medical Campus, has a dedicated office that provides guidance for long-term career planning, grant-writing workshops and resources for wellness management.What's next for you?In addition to mice, I am using zebrafish to study mechanisms of craniofacial development and disorders. I am currently generating zebrafish models for several craniofacial disorders and zebrafish lines that express fluorescent signalling reporters, including the one used in this study, so that we can monitor signalling events in real time during normal and abnormal development.


Gluconobacter oxydans only recently the first plasmids became available. These systems solely enable AraC- and TetR-dependent induction. In this study we showed that the l-rhamnose-dependent regulator RhaS from Escherichia coli and its target promoters PrhaBAD, PrhaT, and PrhaSR could also be used in G. oxydans for regulatable target gene expression. Interestingly, in contrast to the responsiveness in E. coli, in G. oxydans RhaS increased the expression from PrhaBAD in the absence of l-rhamnose and repressed PrhaBAD in the presence of l-rhamnose. Inserting an additional RhaS binding site directly downstream from the −10 region generating promoter variant PrhaBAD(+RhaS-BS) almost doubled the apparent RhaS-dependent promoter strength. Plasmid-based PrhaBAD and PrhaBAD(+RhaS-BS) activity could be reduced up to 90% by RhaS and l-rhamnose, while a genomic copy of PrhaBAD(+RhaS-BS) appeared fully repressed. The RhaS-dependent repression was largely tunable by l-rhamnose concentrations between 0% and only 0.3% (w/v). The RhaS-PrhaBAD and the RhaS-PrhaBAD(+RhaS-BS) systems represent the first heterologous repressible expression systems for G. oxydans. In contrast to PrhaBAD, the E. coli promoter PrhaT was almost inactive in the absence of RhaS. In the presence of RhaS, the PrhaT activity in the absence of l-rhamnose was weak, but could be induced up to 10-fold by addition of l-rhamnose, resulting in a moderate expression level. Therefore, the RhaS-PrhaT system could be suitable for tunable low-level expression of difficult enzymes or membrane proteins in G. oxydans. The insertion of an additional RhaS binding site directly downstream from the E. coli PrhaT −10 region increased the non-induced expression strength and reversed the regulation by RhaS and l-rhamnose from inducible to repressible. The PrhaSR promoter appeared to be positively auto-regulated by RhaS and this activation was increased by l-rhamnose. In summary, the interplay of the l-rhamnose-binding RhaS transcriptional regulator from E. coli with its target promoters PrhaBAD, PrhaT, PrhaSR and variants thereof provide new opportunities for regulatable gene expression in G. oxydans and possibly also for simultaneous l-rhamnose-triggered repression and activation of target genes, which is a highly interesting possibility in metabolic engineering approaches requiring redirection of carbon fluxes.For regulatable target gene expression in the acetic acid bacterium (AAB)  Gluconobacter oxydans harbors the beneficial ability of regio- and stereoselective incomplete oxidation of a variety of sugars, sugar alcohols and other substrates in the periplasm by membrane-bound dehydrogenases (mDHs) and release of resulting products into the cultivation medium  n medium . Therefomiglitol . The indrecently .G. oxydans, only constitutive promoters were used in the past due to the lack of a regulatable promoter. For expression, derivatives of the pBBR1MCS plasmid family obtained from the endogenous plasmid pBBR1 from Bordetella bronchiseptica were the most successful shuttle and expression vectors used  to consensus recognition sequences found in all three Prha promoters and interaction of CRP with the RNA polymerase, which depends on the bending of the promoter DNA by RhaS or RhaR. In G. oxydans CRP is absent since the predicted CRP gene (GOX0974/GOX_RS06010) was shown to encode an iron–sulfur cluster protein termed GoxR, an FNR-type transcriptional regulator of genes involved in respiration and redox metabolism 2SO4, and 2.5 g L−1 MgSO4 × 7 H2O at 30°C. The initial pH of the medium was set to 6 by the addition of KOH (5 M stock). Because G. oxydans possesses a natural resistance toward cefoxitin, 50 μg ml−1 of the antibiotic was routinely added to the medium as a precaution to prevent bacterial contaminations. Stock solutions of cefoxitin (50 mg ml−1) and d-mannitol (200 g L−1) were sterile-filtered and added to autoclaved medium. Unless stated otherwise, for shake flask cultivations cells from 10 ml overnight pre-cultures were used to inoculate 50 ml d-mannitol medium in 500 ml shaking flasks with three baffles to an initial optical density at 600 nm (OD600) of 0.3 . All shake flasks cultures were grown on a rotary shaker at an agitation speed of 180 rpm. G. oxydans cells harboring pBBR1MCS-5-based plasmids were supplemented with 10 μg ml−1 gentamicin (Escherichia coli strains were cultivated at 37°C and 160 rpm in lysogeny broth (LB) medium. Medium of E. coli carrying pBBR1MCS-5-based plasmids was supplemented with 10 μg ml−1 gentamicin. Escherichia coli S17-1 was used as donor strain to transform G. oxydans by conjugation  was used according to the manufacturer’s protocol. The correctness of the plasmid inserts was checked by DNA sequencing (Eurofins MWG).All DNA oligonucleotides used in this study were obtained from Eurofins Genomics and are listed in TGOX0028 . All DNAgdhM-MCS-TGOX0028 that we created previously for the TetR-Ptet system (gdhM) and GOX0028 (TGOX0028) flank the multiple cloning site (MCS) to reduce potential interferences caused by genetic elements on the plasmid backbone. Unless stated otherwise, pBBR1MCS-5-TgdhM-MCS-TGOX0028 was restricted for insert integration with the restriction endonucleases XbaI and EcoRI. Furthermore, in all constructs using the promoters PrhaBAD, PrhaSR, PrhaT, PGOX0264, or PGOX0452 to express the reporter gene mNeonGreen (mNG), the ribosome binding site (RBS) AGGAGA was placed upstream from mNG and downstream from the naturally occurring RBS of the respective promoter region.In this study, all plasmids were constructed using the vector pBBR1MCS-5-Tt system . The terrhaSR-PrhaSR-PrhaBAD-mNG, two DNA fragments were inserted in pBBR1MCS-5-TgdhM-MCS-TGOX0028: DNA fragment with rhaSR-PrhaSR-PrhaBAD-RBS amplified with the primer pair PF1/PF2 from the genome of E. coli LJ110 and DNA fragment with mNG-TBBa_B1002 amplified with the primer pair PF3/PF4 from pBBR1MCS-5-araC-PBAD-mNG -mNG containing an additional RhaS binding site (+RhaS-BS) directly downstream from the −10 region of PrhaBAD, a DNA fragment consisting of (+RhaS-BS)-RBS-mNG-TBBa_B1002 was amplified with the primer pair PF25/PF4 from pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD-mNG and integrated into the EcoRI-restricted plasmid pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD-mNG. The additional RhaS binding site was introduced by primer PF25.For construction of plasmid pBBR1MCS-5-rhaSR-rhaS was constructed from plasmid pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD-mNG by EcoRI digestion and religation. Two EcoRI sites were located perfectly to remove mNG without the need of further cloning steps.The plasmid pBBR1MCS-5-PrhaT-mNG lacking rhaS was constructed with the DNA fragments PrhaT-RBS generated with the primer pair PF26/PF27 from the genome of E. coli LJ110 and the fragment mNG-TBBa_B1002 generated with the primer pair PF28/PF4 from plasmid pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD-mNG.The plasmid pBBR1MCS-5-PrhaS-PrhaSR-PrhaT-mNG was created with two DNA fragments. The first DNA fragment contained rhaS-PrhaSR amplified with the primer pair PF5/PF29 from pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD-mNG. The second DNA fragment contained PrhaT-RBS-mNG-TBBa_B1002 amplified with the primer pair PF30/PF4 from pBBR1MCS-5-PrhaT-mNG.The plasmid pBBR1MCS-5-rhaS-PGOX0264-PrhaT-mNG was constructed using two fragments. The fragment containing rhaS-PGOX0264 was amplified from template pBBR1MCS-5-rhaS-PGOX0264-PrhaBAD-mNG with the primer pair PF5/PF34. The fragment containing PrhaT-mNG was amplified from template pBBR1MCS-5-rhaS-PrhaSR-PrhaT-mNG with the primer pair PF4/PF37.The plasmid pBBR1MCS-5-rhaS-PGOX0452-PrhaT-mNG was constructed using two fragments. The fragment containing rhaS-PGOX0452 was amplified from template pBBR1MCS-5-rhaS-PGOX0452-PrhaBAD-mNG with the primer pair PF5/PF38. The fragment containing PrhaT-mNG was amplified from template pBBR1MCS-5-rhaS-PrhaSR-PrhaT-mNG with the primer pair PF4/PF39.The plasmid pBBR1MCS-5-rhaS-PGOX0264-PrhaT(−10-RhaS-BS)-mNG was constructed using two fragments. The fragment containing rhaS-PGOX0264 and the 5′ part of PrhaT was amplified from template pBBR1MCS-5-rhaS-PGOX0264-PrhaT-mNG with the primer pair MM14/PF5. The fragment containing the remaining 3′ part of PrhaT followed by mNG was amplified from template pBBR1MCS-5-rhaS-PGOX0264-PrhaT-mNG with the primer pair MM13/PF4. The additional RhaS binding site directly downstream from the −10 region of PrhaT was created and introduced by the primers MM13 and MM14.The plasmid pBBR1MCS-5-rhaS-PGOX0452-PrhaT(−10-RhaS-BS)-mNG was constructed using two fragments. The fragment containing rhaS-PGOX0452 and the 5′ part of PrhaT was amplified from template pBBR1MCS-5-rhaS-PGOX0452-PrhaT-mNG with the primer pair MM14/PF5. The fragment containing the remaining 3′ part of PrhaT followed by mNG was amplified from template pBBR1MCS-5-rhaS-PGOX0452-PrhaT-mNG with the primer pair MM13/PF4. The additional RhaS binding site directly downstream from the −10 region of PrhaT was created and introduced by the primers MM13 and MM14.The plasmid pBBR1MCS-5-rhaS-PGOX0264-PrhaT(TSS-RhaS-BS)-mNG was constructed using two fragments. The fragment containing rhaS-PGOX0264 and the 5′ part of PrhaT was amplified from template pBBR1MCS-5-rhaS-PGOX0264-PrhaT-mNG with the primer pair MM16/PF5. The fragment containing the remaining 3′ part of PrhaT followed by mNG was amplified from template pBBR1MCS-5-rhaS-PGOX0264-PrhaT-mNG with the primer pair MM15/PF4. The additional RhaS binding site directly downstream from the E. coli transcriptional start of PrhaT was created and introduced by the primers MM15 and MM16.The plasmid pBBR1MCS-5-rhaS-PGOX0452-PrhaT(TSS-RhaS-BS)-mNG was constructed using two fragments. The fragment containing rhaS-PGOX0452 and the 5′ part of PrhaT was amplified from template pBBR1MCS-5-rhaS-PGOX0452-PrhaT-mNG with the primer pair MM16/PF5. The fragment containing the remaining 3′ part of PrhaT followed by mNG was amplified from template pBBR1MCS-5-rhaS-PGOX0452-PrhaT-mNG with the primer pair MM15/PF4. The additional RhaS binding site directly downstream from the E. coli transcriptional start of PrhaT was created and introduced by the primers MM15 and MM16.The plasmid pBBR1MCS-5-mNG for genomic labelling of G. oxydans 621H by mNG under control of PrhaBAD(+RhaS-BS) in igr3 was constructed with three fragments. The upstream and downstream flanking regions of igr3 were amplified from genomic DNA of G. oxydans 621H with the primer pairs PF31/PF32 and PF33/PF34, respectively. The fragment containing PrhaBAD(+RhaS-BS)-mNG was amplified from plasmid pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD(+RhaS-BS)-mNG with the primer pair PF35/PF36.The plasmid pKOS6b-igr3::GOX0264-rhaS for genomic integration of a PGOX0264-rhaS copy into igr2 of G. oxydans mNG was constructed with three fragments. The upstream and downstream flanking regions of igr2 were amplified from genomic DNA of G. oxydans 621H with the primer pairs PF40/PF41 and PF42/PF43, respectively. The fragment containing PGOX0264-rhaS was amplified from plasmid pBBR1MCS-5-rhaS-PGOX0264-PrhaBAD-mNG with the primer pair PF44/PF45.The plasmid pKOS6b-igr2::PrhaSR-rhaS for genomic integration of a PrhaSR-rhaS copy into igr2 of G. oxydans mNG was constructed with three fragments. The upstream and downstream flanking regions of igr2 were amplified from genomic DNA of G. oxydans 621H with the primer pairs PF40/PF46 and PF42/PF43, respectively. The fragment containing PrhaSR-rhaS was amplified from plasmid pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD-mNG with the primer pair PF44/PF47.The plasmid pKOS6b-igr2::PGOX0264-rhaS for genomic integration of a PGOX0264-rhaS copy into igr1 of G. oxydans mNG igr2::PGOX0264-rhaS was constructed with three fragments. The upstream and downstream flanking regions of igr1 were amplified from genomic DNA of G. oxydans 621H with the primer pairs MH3/MH10 and MH6/MH9, respectively. The fragment containing PGOX0264-rhaS was amplified from plasmid pBBR1MCS-5-rhaS-PGOX0264-PrhaBAD-mNG with the primer pair MH4/MH5.The plasmid pKOS6b-igr1::PrhaSR-rhaS for genomic integration of a PrhaSR-rhaS copy into igr1 of G. oxydans mNG igr2::PGOX0264-rhaS was constructed with three fragments. The upstream and downstream flanking regions of igr1 were amplified from genomic DNA of G. oxydans 621H with the primer pairs MH7/MH10 and MH6/MH9, respectively. The fragment containing PrhaSR-rhaS was amplified from plasmid pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD-mNG with the primer pair MH5/MH8.The plasmid pKOS6b-igr1::PG. oxydans 621H and selection of excised plasmid backbones were carried out using pKOS6b plasmid derivatives and counterselection by cytosine deaminase, encoded by codA from E. coli, in the presence of the fluorinated pyrimidine analogue 5-fluorocytosine (FC). The cytosine deaminase converts nontoxic FC to toxic 5-fluorouracil, which is channeled into the metabolism by the uracil phosphoribosyltransferase, encoded by the chromosomal upp gene of Gluconobacter. The details of the method are described elsewhere  using a 40% (w/v) stock solution. Equal volumes of medium were added to non-supplemented reference cultures. Throughout the cultivation, growth (OD600) and fluorescence emission were monitored in intervals using a spectrophotometer  and an Infinite M1000 PRO Tecan reader . For microscale BioLector cultivations, overnight starter cultures were used to inoculate 800 μl batches of d-mannitol medium in 48-well Flowerplates® (m2p-labs) to an initial OD600 of 0.3. Sealed with disposable foil (m2p-labs), plates were cultivated for 24 h at 1,200 rpm, 85% humidity and 30°C. Growth was monitored in each well as backscattered light at 620 nm (A620 nm) and protein fluorescence was monitored as emission (λex 510 nm/λem 532 nm). For backscatter signal amplification, gain 20 was applied. Signal amplification of fluorescence emission varied (gain 40–70) and is indicated in the figure legends. All BioLector data shown in a diagram were measured in the same run of a growth experiment.The regulation and relative strength of the promoters on constructed plasmids was monitored in tein mNG . For anaG. oxydans 621H harboring either plasmid pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD-mNG or pBBR1MCS-5-rhaS-PrhaSR-PrhaT-mNG. The FACS was operated with a 70 μm nozzle and run with a sheath pressure of 70 psi. The forward scatter (FSC) and side scatter (SSC) were recorded as small-angle scatter and orthogonal scatter, respectively, by means of a 488 nm solid blue laser beam. For analysis, only particles/events above 200 a.u. for FSC-H and above 300 a.u. for SSC-H as the thresholds were considered. The mNG fluorescence emission was detected from the SSC through the combination of a 502 nm long-pass and 530/30 nm band-pass filter. Prior to data acquisition, the FSC-A vs. SSC-A plot was employed to gate the population and to exclude signals originating from cell debris or electronic noise. In a second and third gating step, from the resulting population, the SSC-H signal was plotted against the SSC-W signal and this population was subsequently gated in a FSC-H vs. FSC-W plot to exclude doublets. From this resulting singlet population, 100,000 events were recorded at a rate of <10,000 events/s for fluorescence data acquisition. For data analysis and visualization of all gated events  FlowJo 10.7.2 for Windows  was applied.For single cell analysis, a FACSAria™ cell sorter controlled by FACSDiva 8.0.3 software (BD Biosciences) was used to analyze the mNG reporter protein signals in G. oxydans cells were grown to an OD600 of 1.3, centrifuged  and washed twice with 50 mM phosphate buffer (pH 6). After the second washing step, cells were resuspended in biotransformation buffer  supplemented with 2% (w/v) l-rhamnose and incubated for 24 h at 30°C and 200 rpm. Then, the cells were removed from the buffer  and the supernatant was used for analysis by gas chromatography  coupled to a Waters Micromass GCT Premier high-resolution time-of-flight mass spectrometer (Waters). Sample handling for derivatization, GC-TOF-MS operation, and peak identification were carried out as described . DNA concentrations were measured using a Qubit 2.0 fluorometer (Thermo Fisher Scientific). Illumina sequencing and data analysis of the indicated Pescribed . For theere used .G. oxydans cells carrying plasmid pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD(+RhaS-BS)-mNG were cultivated in shake flasks with 50 ml complex d-mannitol medium. Cells were harvested at OD600 of 1.5 in the mid-exponential phase and total RNA was extracted as described (G. oxydans genome and the pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD(+RhaS-BS)-mNG plasmid sequence using the RNA-seq analysis tool implemented in the CLC software. Read mapping settings used were 80% length fraction and 80% similarity fraction. The starts of mapped reads and total nucleotide coverage according to the mappings were used to assess transcriptional starts on the expression plasmid with the promoters PrhaSR and PrhaBAD(+RhaS-BS).escribed . The RNArhaBAD system from E. coli responds to the monosaccharide rhamnose in the uncommon l conformation, which is similar to the AraC-ParaBAD system and its effector l-arabinose. Like l-arabinose, the inducer l-rhamnose needs to enter the cell to interact with its targeted regulators RhaR and RhaS  l-rhamnose instead of d-mannitol, there was no growth of G. oxydans 621H and the initial start OD600 of 0.04 did not change within 24 h (d-mannitol medium supplemented with 1% (w/v) l-rhamnose, the strain 621H grew very similar and without a significant difference compared to the growth in the d-mannitol complex medium without l-rhamnose supplement. Furthermore, with and without l-rhamnose the initial pH 6 of the growth medium was acidified to pH 4.3 after 24 h, suggesting no relevant oxidation of l-rhamnose to a corresponding acid. Therefore, there was no negative or supportive effect of l-rhamnose on the growth of G. oxydans 621H up to 1% (w/v).The results confirmed that ter 24 h . Hence, hin 24 h . In 4%  was used to measure the PrhaBAD-controlled expression by placing the mNG gene downstream from PrhaBAD. On the plasmid, the elements rhaSR-PrhaSR-PrhaBAD-mNG were flanked by three terminators, TgdhM downstream from rhaR, and TBBa_B1002 and TGOX0028 downstream from mNG  l-rhamnose. Overnight pre-cultures were split to inoculate main cultures in d-mannitol medium with and without l-rhamnose. Growth and mNG fluorescence was monitored in a BioLector.First, we tested the inducibility of Pfrom mNG . Further systems , 2021b. l-rhamnose supplementation  was observed  l-rhamnose, the mNG fluorescence was ~2,200-fold higher compared to the mNG fluorescence in cultures without l-rhamnose (data not shown). To verify that the reversed responsiveness of RhaSR-PrhaBAD indeed was observed in G. oxydans 621H carrying the intended plasmid without mutations possibly acquired later during growth, G. oxydans cells of an induced culture were harvested at the end of the cultivation (24 h) for isolation of total DNA and Illumina sequencing. The read data analysis excluded unexpected contamination of the culture, since 99.48% of 1,402,738 trimmed and quality-filtered reads mapped to the updated reference sequences of the G. oxydans 621H genome (88-fold coverage), the 5 endogenous plasmids, and the mNG expression plasmid with rhaSR-PrhaSR-PrhaBAD-mNG . In rhaS there was the silent mutation of CTG to CTT (Leu166) and the mutation of GGG to TGG resulting in the exchange Gly136Trp in RhaS. All three mutations were present already on the plasmid when it was cloned in E. coli. To exclude an effect of these mutations on the reversed responsiveness only in G. oxydans, the plasmid was cloned again using a new rhaS DNA template from E. coli MG1655. This plasmid lacked the two point mutations in rhaS and also showed the reversed responsiveness in G. oxydans 621H with the same extent of repression (data not shown). Thus, the DNA point mutations in rhaS did not affect the regulatory properties of the system in G. oxydans. In summary, these results showed that in contrast to E. coli the PrhaBAD promoter is repressed in G. oxydans in the presence of l-rhamnose.To test whether the G. oxydans protein is responsible for the reversed responsiveness of the RhaSR-PrhaBAD system, we constructed derivatives of the expression plasmid either lacking in-frame a substantial part of rhaS, or lacking rhaR, or lacking both genes, yet keeping all the elements upstream and downstream from rhaS and rhaR (G. oxydans clones carrying one of these plasmid derivatives were grown in d-mannitol medium without and with 1% (w/v) l-rhamnose and cultivated in a BioLector to monitor growth and mNG fluorescence. Regardless of the plasmid used, all G. oxydans cultures exhibited very similar growth with and without l-rhamnose , regardless of l-rhamnose supplementation , suggesting a general negative effect of RhaR on the PrhaBAD activity regardless of the presence or absence of l-rhamnose. This is in line with the observation that with the plasmid lacking only rhaR, expression from PrhaBAD increased in the absence of l-rhamnose by ~20% (513 a.u.) compared to the plasmid with both regulator genes (431 a.u.). Furthermore, with 1% (w/v) l-rhamnose the mNG expression from PrhaBAD was more reduced with the rhaS-PrhaBAD construct (94 a.u.) than with the rhaSR-PrhaBAD construct (122 a.u.).To analyze whether RhaS and/or RhaR, or an interfering endogenous and rhaR . G. oxydrhamnose . The difentation . The clorhaBAD promoter in the absence of l-rhamnose and represses PrhaBAD in the presence of l-rhamnose, and thus is exerting a dual role in G. oxydans  in the absence of l-rhamnose. These results suggested that PrhaSR is a very strong promoter per se, or because it is positively auto-regulated by RhaS. However, the RhaS protein was reported to severely aggregate when overexpressed . The mNG expression obtained with rhaS-PGOX0264-PrhaBAD-mNG and with rhaS-PGOX0452-PrhaBAD-mNG was reduced by 77% (from 212 to 48 a.u.) and by 68% (from 95 to 30 a.u.), respectively (In the presence of 1% (w/v) ectively .rhaSR in G. oxydans and the influence of RhaS on PrhaSR activity, we created plasmids with mNG under the control of PrhaSR and with rhaS under the control of the constitutive promoters PGOX0452 or PGOX0264, or lacking rhaS  l-rhamnose. This l-rhamnose-dependent increase was less pronounced with rhaS under control of the stronger PGOX0264 where the RhaS level was expected to be higher. Here, the mNG fluorescence increased only 1.3-fold from 144 to 187 a.u.  l-rhamnose in a BioLector (l-rhamnose strongly reduced the mNG fluorescence after ~7 h by 75% (225 vs. 55 a.u.). This indicated that the RhaS-PrhaBAD system is quite sensitive and already low l-rhamnose concentrations should enable a tuning of target gene repression. Supplementation with 1% and 3% (w/v) l-rhamnose reduced mNG fluorescence by 83% (38 a.u.) and 85% (34 a.u.), respectively. This suggested that already 1% (w/v) l-rhamnose was sufficient to reach almost maximal possible repression of plasmid-based PrhaBAD copies in G. oxydans.Since from all tested plasmid variants the one lacking ioLector ,B. AlrearhaBAD-based expression toward relatively low l-rhamnose concentrations was also observed in shake flask cultivations. When grown in 50 ml d-mannitol medium supplemented with 0.25% l-rhamnose, the mNG fluorescence was reduced to 24%  after 9 h .This responsiveness of Pfter 9 h ,D. In shrhaBAD-derived mNG expression on the single cell level. In the absence of l-rhamnose, 7 h after inoculation 95.5% of the analyzed cells showed strong mNG fluorescence , while when grown with 1% (w/v) l-rhamnose, 96.4% of the analyzed cells showed a 89% reduced fluorescence  directly downstream from the annotated E. coli −10 region of PrhaBAD. Additional binding of the RhaS-l-rhamnose complex downstream from the −10 region should potentially contribute to the repression of PrhaBAD. Also, it was interesting to see the general impact of this additional RhaS BS on the PrhaBAD activity in the absence of l-rhamnose.In an attempt to reduce the residual expression from PrhaS-PrhaSR-PrhaBAD-mNG as template and created a copy of the 50 bp region comprising the native RhaS-BS present in PrhaBAD. This copy was inserted directly downstream from the −10 region of PrhaBAD. The resulting plasmid was termed pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD(+RhaS-BS)-mNG and its expression performance was compared with that of the template plasmid (d-mannitol medium without and with 1% (w/v) l-rhamnose, both strains showed similar growth independent of the plasmids or l-rhamnose supplementation  that of the parental plasmid (225 a.u.), suggesting additional activation of PrhaBAD by RhaS in the absence of l-rhamnose or a new transcriptional start increasing the mNG expression .We used plasmid pBBR1MCS-5- plasmid . In d-maentation . Interespression . In the rhaBAD(+RhaS-BS) repression was tested with 0.05%, 0.1%, 0.2%, 0.3%, 1%, and 3% (w/v) l-rhamnose  is also almost maximally repressed by 1% (w/v) l-rhamnose. The calculated residual mNG expression from PrhaBAD(+RhaS-BS) was 11% and 10%, respectively. With 0.3% (w/v) l-rhamnose, the residual mNG fluorescence was 17% (405 vs. 69 a.u.). With only 0.05% (w/v) l-rhamnose, the mNG fluorescence was reduced approximately by half (from 406 to 197 a.u.), showing the sensitivity and tunability of the system. In shake flask cultivations with 0.25% and 1% (w/v) l-rhamnose, PrhaBAD(+RhaS-BS) showed a similar repression performance as in microscale BioLector conditions. After 9 h of growth in shake flasks, the maximal mNG fluorescence without l-rhamnose  was reduced to 1,060 and 600 a.u. in the presence of 0.25% and 1% (w/v) l-rhamnose -derived mNG fluorescence vs. the l-rhamnose concentrations illustrates the responsiveness of both promoters toward low l-rhamnose concentrations (rhaBAD(+RhaS-BS) was two-fold stronger than PrhaBAD and therefore offers a wider dynamic range of expression.Plotting the relative maximal Ptrations . While trhaBAD variant can be completely repressed in a plasmid-free strain when this modified target promoter and rhaS are genomically integrated and present as a single copy instead of being present on a plasmid with medium copy number (mNG under control of PrhaBAD(+RhaS-BS) into the intergenic region igr3 (GOX0038/GOX_RS01330–GOX0039/GOX_RS01335). The resulting strain was termed G. oxydans mNG. For single-copy rhaS expression, we tested the promoters PrhaSR and PGOX0264 and integrated both rhaS constructs in G. oxydans mNG separately into igr2 (GOX0028/GOX_RS01280–GOX0029/GOX_RS01285). The resulting G. oxydans strains mNG igr2::PGOX0264-rhaS and mNG igr2::PrhaSR-rhaS were cultivated and analyzed in a BioLector (l-rhamnose expression of single-copy rhaS under control of PrhaSR resulted in higher activity of PrhaBAD(+RhaS-BS) than with PGOX0264-rhaS. However, with rhaS under control of PGOX0264 a much higher extent of repression was observed with 1% (w/v) l-rhamnose. Here, the maximal mNG signals were reduced by 64% from 217 to 78 a.u. (rhaS expression is not sufficient to completely repress PrhaBAD(+RhaS-BS). We then tested if a second genomic rhaS copy could be sufficient and integrated both PGOX0264-rhaS and PrhaSR-rhaS into strain mNG igr2::PGOX0264-rhaS separately into igr1 (GOX0013/GOX_RS01200–GOX0014/GOX_RS01205). The two resulting G. oxydans strains mNG igr1::PGOX0264-rhaS igr2::rhaS and mNG igr1::PrhaSR-rhaS igr2::rhaS were cultivated and analyzed in a BioLector . With one copy of PrhaSR-rhaS and one copy of PGOX0264-rhaS the maximal mNG signals were reduced by 78% from 435 to 96 a.u. With two genomic copies of PGOX0264-rhaS, the maximal mNG signals were reduced by 84% from 444 to 73 a.u.  can be completely repressed at all, we constructed the rhaS expression plasmid pBBR1MCS-5-PrhaSR-rhaS and introduced it into the single-copy rhaS strain G. oxydans mNG igr2::PGOX0264-rhaS already showing 64% promoter repression (rhaBAD(+RhaS-BS) appeared completely repressed by 3% and possibly also by 1% (w/v) l-rhamnose. To test the tunability of this repression with plasmid-based expression of rhaS, we also tested lower l-rhamnose concentrations  l-rhamnose, the maximal mNG signals were reduced by 78% from 216 to 47 a.u.. These results indicated a relatively high sensitivity of the system toward lower l-rhamnose concentrations and that a genomic copy of the RhaS target promoter variant can be tuned.To test if a genomic single-copy Ppression . The respression ,C. Accortrations . In the E. coli RhaS also activates the promoter PrhaT of the l-rhamnose transporter gene rhaT. Similar to PrhaBAD, PrhaT contains two regulatory elements, one for RhaS and one for CRP binding. Contrary to PrhaBAD, the RhaS binding site on PrhaT is differently composed and slightly shifted, so that the binding site does not overlap with the −35 element of PrhaT  l-rhamnose increased mNG fluorescence ~7.5-fold (from 36 to 266 a.u.) within 8 h. The values indicated a weak or moderate strength of PrhaT in G. oxydans . Again, growth of G. oxydans cells with pBBR1MCS-5-rhaS-PrhaSR-PrhaT-mNG was largely unaffected by up to 2% (w/v) l-rhamnose  l-rhamnose already half of the maximal induction was reached showing that the weak to moderate PrhaT-derived mNG expression could be nicely tuned by low l-rhamnose concentrations  l-rhamnose . In the presence of 1% (w/v) l-rhamnose, 96.9% of the population showed approximately 9-fold higher mNG fluorescence signals . We also tested the inducible PrhaT-derived mNG expression in shake flask cultures with 0.3% and 1% (w/v) l-rhamnose. Under these conditions, all cultures with pBBR1MCS-5-rhaS-PrhaSR-PrhaT-mNG exhibited very similar growth (mNG expression was similarly induced as in the BioLector cultivations (l-rhamnose (50 vs. 297 a.u.), respectively.The homogeneity of Prhamnose . In the r growth . The mNGivations . The maxrhaS expression strength from different promoters on the performance of the RhaS-PrhaT system, we replaced PrhaSR and constructed plasmid variants with PGOX0264-rhaS and PGOX0452-rhaS  l-rhamnose were ~25% lower compared to that obtained with PrhaSR-rhaS. Since the non-induced maximal mNG signals obtained with PrhaT were somewhat higher with PGOX0264-rhaS (46 a.u.) and were approximately 3-fold higher with PGOX0452-rhaS (104 a.u.) compared to PrhaSR-rhaS (36 a.u.), the maximal induction fold changes with 4% (w/v) l-rhamnose were only 5-fold with PGOX0264-rhaS and 2.4-fold with PGOX0452-rhaS. Thus, compared to PrhaSR-rhaS the non-induced basal expression level was not lowered and the induction fold changes of the RhaS-PrhaT system were not improved by using PGOX0264 or PGOX0452 for rhaS expression.To test the influence of 452-rhaS . The G. f PrhaSR –E. For brhaT, we inserted the RhaS binding site sequence from PrhaBAD on the one hand directly downstream from the E. coli −10 region (−10-RhaS-BS) and on the other hand downstream from the E. coli TSS (TSS-RhaS-BS), and constructed for both PrhaT variants expression plasmids with rhaS under control of either PGOX0264 or PGOX0452 (rhaT(−10-RhaS-BS) was repressible. The maximal mNG signals in the absence of l-rhamnose for both rhaS constructs PGOX0264-rhaS (250 a.u.) and PGOX0452-rhaS (214 a.u.) were reduced by 65%  was still inducible, yet showed increased and relatively high non-induced mNG signals in the absence of l-rhamnose, which could maximally only be doubled by induction with 4% (w/v) l-rhamnose  can interact with the RNA polymerase (RNAP) and thereby activates transcription of rhaSR  of genes involved in respiration and redox metabolism  subunit RpoD of the RNAP. This mode of activation is often indicated by regulator binding sites overlapping with the −35 element of the target promoter  to the other side of the DNA strand when comparing the theoretical binding of RhaS and σ70-RNAP to PrhaBAD with the binding to PrhaT. Because of this theoretical difference in the orientation of RhaS toward σ70-RNAP, RhaS possibly interacts with the α-CTD of the RNAP in the case of PrhaT and with σ70 in the case of PrhaBAD. Since the α-CTD and σ70 from G. oxydans and E. coli differ to some extent, the conformational changes of RhaS induced by the binding of l-rhamnose may affect the interactions of RhaS with the α-CTD and with σ70 from G. oxydans differently compared to the interactions with the α-CTD and with σ70 from E. coli, finally resulting in the different modes of the regulation of PrhaBAD and PrhaT in G. oxydans. Interestingly, in the case of PrhaSR, the RhaR binding site also overlaps with the −35 region as the RhaS binding site in PrhaBAD. Moreover, one of the major groove regions of each RhaR half site on PrhaSR is nearly identical to the corresponding half site for RhaS binding on PrhaBAD and RhaS can also bind to the RhaR binding site in PrhaSR as mentioned above  within the three E. coli promoter regions PrhaBAD, PrhaT, and PrhaSR in G. oxydans is required to better explain the effects, including the activation of PrhaBAD by RhaS in the absence of l-rhamnose, the repression, and the effects of the additional RhaS binding site inserted into PrhaBAD and PrhaT.As mentioned above, in Pupstream . These ded above . DespiteG. oxydans 621H with plasmid pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD(+RhaS-BS)-mNG cultivated in the complex medium with d-mannitol in the absence of l-rhamnose and harvested in the mid-exponential phase. The RNA sample was sent to Vertis Biotechnologie AG for sample processing and Illumina sequencing to obtain high-quality TSS data . The resulting fastq file comprised 10,255,084 reads (75 bp). After reads trimming and quality filtering, 1,023,259 reads mapped to the sequence of pBBR1MCS-5-rhaS-PrhaSR-PrhaBAD(+RhaS-BS)-mNG. The overall reads mapping showed three prominent reads stacks indicating the three most active transcriptional starts on the plasmid (gmR (aacC1) conferring gentamycin resistance and was oriented toward gmR. The second-highest stack  was upstream from rhaS and oriented toward rhaS. In contrast to the expectation for rhaS, the start position of this stack was not within PrhaSR, but upstream from PrhaSR within the PrhaBAD region between its −35 and −10 regions from E. coli. The third-highest stack  was found within the coding region of rhaS and oriented toward the 3′ end of rhaS. For PrhaBAD, the insertion of an additional RhaS binding site could possibly generate an additional transcriptional start site in G. oxydans enabling the two-fold increased mNG signals described above for PrhaBAD(+RhaS-BS). However, in contrast to the expectations, no one or two major TSSs with high coverage toward mNG corresponding to the reported E. coli TSS and a potential new TSS could be seen. Instead, the detailed reads mapping showed several reads stacks of only medium coverage, partially with scattering start positions, in the PrhaBAD(+RhaS-BS) region and the 5′ region of mNG , yet it is likely that also with PrhaBAD and PrhaT(−10-RhaS-BS) complete repression of a genomic copy could be achieved. These promoters cover different ranges of expression strength, which could be selected according to the requirements of the genomic target gene. Tunable and complete promoter repression is also useful for the functional study of essential genes that cannot be deleted. Optimizing genomic rhaS expression or further increasing the genomic rhaS copy number beyond two to achieve a sufficient RhaS level may finally overcome the necessity of plasmid-based rhaS expression to achieve complete chromosomal promoter repression. Furthermore, more TSS data sets and deeper analysis are required to better understand the regulations of the target promoters by RhaS in G. oxydans. The TSS results also suggested to analyze the TSSs of heterologous promoters when they are transferred and used in G. oxydans or AAB in general. It can be expected that TSS data sets will help to better understand and overcome the difficulties in getting transferred heterologous regulatable expression systems functional and high-performant in AAB.Summing up and looking ahead, in The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. The Illumina sequencing data are available in the NCBI sequence read archive via the accession numbers PRJNA854345 and PRJNA854679.TP and PF designed and supervised the study. PF, MLG, MM, and MH carried out cloning and experiments. JG performed the GC-TOF analysis. PF, MLG, MM, MH, and TP performed data analysis. PF and TP wrote the manuscript. All authors contributed to the article and approved the submitted version.We are grateful to the Federal Ministry of Education and Research (BMBF) for financial support of the project IMPRES (031B0370B). The funding organization did not influence the design of the study or collection, analysis, and interpretation of data, or writing the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Noise management associated with input signals in sensor devices arises as one of the main problems limiting robot control performance. This article introduces a novel neuromorphic filter model based on a leaky integrate and fire (LIF) neural model cell, which encodes the primary information from a noisy input signal and delivers an output signal with a significant noise reduction in practically real-time with energy-efficient consumption. A new approach for neural decoding based on the neuron-cell spiking frequency is introduced to recover the primary signal information. The simulations conducted on the neuromorphic filter demonstrate an outstanding performance of white noise rejecting while preserving the original noiseless signal with a low information loss. The proposed filter model is compatible with the CMOS technology design methodologies for implementing low consumption smart sensors with applications in various fields such as robotics and the automotive industry demanded by Industry 4.0. The term neuromorphic, coined by Mead , refers Traditional analog filters are designed based on scaling specific frequency domain signal components and attenuating the rest. This approach has been proven effective with noise that is primarily out of signal frequency range. However, linear filters cannot clear noisy signals when disturbance affectation is in the same frequency range as primary signal. In this context, digital filters, especially average filter techniques, take precedence at the cost of resources expense. For this reason, some filter proposals based on the use of several SNs have been made  to a reference voltage level (VRef). It also assumes the generation of a convenient spike shape, compliant with memristors technology to allow weight adjustment during the learning phase.There are several proposals reported on analog implementations of neural model circuits Abbott, . ThroughIin, flowing at its negative input. At the integration mode, the voltage level at the output of the OPAMP decreases until it reaches a determined threshold voltage, Vthr. The comparator circuit compares the membrane voltage, Vmem, with vthr, to generate a signal activating the Phase Control block when the descending Vref reaches Vthr. At this moment, the Phase Control block commands the Spike Generator block to initiate a spike event with a predefined waveform and it changes the control signals, αfire to ON state, while αint to OFF state. These control signals are complementary. The neural circuit is reconfigured by the current states of αfire and αint. If αint is ON, the OPAMP works in the integration mode, if αfire is ON the neuron is in fire mode. During integration mode the neuron output Vout is set in Vref, at the same time, the spike generator block must hold a Vref at the positive OPAMP input, which is buffered at the negative OPAMP input. In the firing mode, Vout, is connected to Vmem, generating a spike event, feedbacking, Vmem to the negative OPAMP input. At the end of the firing mode, Cmem is reset to a Vref, potential.Wu's neural circuit shown in The equivalent model of the LI section is presented in Equation (1).Vp is the voltage objective, while the circuit is in integration mode Vp corresponds to Vthr.Where Wu's circuit functioning could still be simplified to implement the proposed methodology, performing a noise signal filtering process. The simplification consists of establishing constant delays before switching states and restarting the integration phase. It imposes a period of neuron inactivity corresponding to the refractory period, seen in biological brains. This behavior is modeled as shown below:Once the neuron is in the refractory period, it maintains its state for a predefined period, after which it returns to the previous (integration) state.t0, t1, ..., tn, where 0 ≤ ti ≤ T, with i = 0, 1, 2, ..., n is the i-th spike time in an observed period T . By repeating this operation for a certain number of different stimuli, it is possible to estimate a function, The time between spikes in the circuit presented in Rearranging elements from Equation (1), we find the next expression.Iin = IRLeak+ICmem, where IRLeak(t) is the current across RLeak, which behavior is unknown in advance, thus:Equation (2) corresponds to Current Kirchhoff's Law, producing a summation of all the currents at input node, Now, integrating both sides of Equation (3) with the defined time intervals limits between neural events (spike occurrences) results in Equation (4). Internal values of the neuromorphic units are reset at the end of each neural event,Solving integrals on both sides:Vmem = Vthr−Vref results at the end of the integration period. Equation (5) corresponds to the Mean Value Theorem for integrals  on the same period. Particularly, Iin can be seen as the mean value of current on the period tn−tn−1 plus a constant value (CmemVmem).Where the value Stewart, . Thus, aIin value on Equation (5). The methodology proposed to use a neural circuit as a signal filter is depicted in Our proposal consists of using the tuning curve function of the neuromorphic circuit to estimate the Iin of Equation (1) between a current interval ∈μA, considering the following electrical and timing parameters: Cmem = 1μF, Rleak = 10kΩ, and a refractory period of 10μs, we proceed to measure the time elapsed between potential membrane spikes. The below equation is proposed as a prototype to estimate function f.The tuning curve for the circuit introduced in a = 1724.8761, b = 21.6051, c = −161.1285, are determined using nonlinear least squares curve fitting .Because Equation (6) is invertible, we can take two produced spikes and calculate the current in the elapsed period between spikes. That is to say, Where:p = tn−tn−1. tn is the time of the n-th spike. In order to maintain values on a more convenient time-scale α = 1 × 104 and β = 1 × 10−6 are added as scale factors. Equation (7) is evaluated at each spike and the value is held until the next spike occurs, refer to with: Observe that Equation (7) is a decaying exponential function; therefore, it is possible to define a circuit that reproduces this behavior by using the capacitor discharging dynamic in a commuted capacitor scheme working as follows. At each spike event, a low impedance branch quickly charges a capacitor during the refractory period of the SN unit. Once the refractory period concludes, the charging branch for the capacitor is open, and discharge becomes through a branch with fixed impedance such that the current on the capacitor has a behavior similar to Equation (7), refer to This scheme based on frequency shows a better performance than other strategies previously introduced . As each filter introduces a different amount of time delay caused by the filtering process, Euclidean distance is not an appropriate choice since filtering techniques with a minimum delay will tend to render better results. Therefore, Fast Dynamic Time Wrapping  minimizing digital electronics, thus reducing the required number of transistors. Our frequency base decoding scheme has proven to have a good noise rejection, specially added white noise, but maintaining good performance with other types of noise, bringing artificial intelligence closer to circuit technology to deliver innovative solutions to filter white noise with the same frequency domain as the original signal, with minimal latency and low information loss. It is also a promising approach to, i.e., the conception of future innovative lab-on-chip implementations. Increasing the signal-to-noise ratio rejection ratio, cost efficiency, and sensitivity, is essential in these devices.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.LG-S proposed, developed, programmed the neural filter code, conducted the simulation runs, and wrote the first draft of the manuscript. VP-P and HS proposed modifications to the encoding and decoding strategy architectures. ER-E reviewed the test bench for the filter experiments. JM-N helped with the neural modeling in Python. All authors contributed to the conception and design of the study, manuscript revision, read, and approved the submitted version.The authors would like to thank the economic support of the projects SIP 20210124, 20221780, 20211657, 20220268, 20212044, 20221089, 20210788, 20220226, and COFAA and CONACYT FORDECYT-PRONACES 6005.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer AZ declared a shared affiliation with the authors to the handling editor at the time of review.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


To evaluate the safety and efficacy of recannulating the axillary artery in reoperative proximal thoracic aortic surgery.Between 2008 and 2020, we evaluated patients who underwent reoperative proximal thoracic aortic surgery. The patients were divided into 2 groups: (i) patients with no previous right axillary artery cannulation (primary cannulation group) and (ii) patients with a previous cannulated right axillary artery (recannulation group). We analysed the intraoperative data, cannulation-related complications, postoperative outcomes and compared the 2 groups (primary cannulation versus recannulation).n = 132) baseline characteristics did not differ significantly between the 2 groups. There was no statistically significant difference in regard to the duration of surgery, cardiopulmonary bypass, aortic cross-clamp and antegrade cerebral perfusion time nor in regard to the total number of patients with cannulation-related complications between the 2 groups . The incidence of iatrogenic axillary artery dissection, iatrogenic aortic dissection, iatrogenic aortic dissection leading to death, axillary artery thrombosis, need for surgical repair, brachial plexus injury rates, malperfusion, high perfusion resistance and hyperperfusion syndrome revealed no significant difference between the 2 groups (P > 0.05). There were 11 (11.0%) cases of stroke in the primary cannulation group and 1 (3.1%) in the recannulation group (P = 0.18).The patient  has emerged as a routine method in aortic surgery for establishing selective antegrade cerebral perfusion (ACP) [1–4]. The cannulation of the artery was performed by using a side-graft in 69 (69.0%) patients in the primary cannulation group and in 30 (93.8%) in the recannulation group, P = 0.005.Intraoperative data are summarized in Table n = 8 (8.0%) vs n = 1 (3.1%), P = 0.34] (Table P > 0.05). No case of axillary cannulation attempt and break-off for technical reasons was documented or remembered by the aortic team.There was no statistically significant difference in regard to the total number of patients with cannulation-related complications between the primary cannulation group and recannulation group [vs n = 1 .1%, P = P = 0.18. Other postoperative complications also revealed no statistically significant differences. The need for transfusion of erythrocyte concentrates and platelet concentrates were numeric slightly higher in the recannulation group but did not attained statistical significance .The postoperative outcomes are summarized in Table In the present study, we compared clinical outcomes of recannulation of the RAA with primary cannulation of the RAA in reoperative proximal thoracic aortic surgery and found no statistically significant differences neither in regard to cannulation-related complications nor postoperative complications.Aortic and cardiac reoperations are complex procedures with high operative risk requiring careful preoperative planning, preventive strategies and compensatory rescue measures. Safely and promptly establishing CPB is of the utmost importance, especially in the event of a re-entry catastrophic injury allowing the repair and limiting the haemodynamic instability. It has been previous shown that careful preoperative planning using computed tomography scanning and using peripheral arterial cannulation is associated with less injuries to vital structures .RAA is supported by growing evidence as the arterial cannulation site of first choice in aortic surgery. It allows establishing selective ACP, avoiding flow reversal and it has been shown to be more beneficial than other cannulation sites in terms of early embolic stroke, mortality and neurological dysfunction , 2, 10. In reoperative aortic surgery, cannulation of the RAA represents both a preventive strategy and a compensatory rescue measure—selective ACP and hypothermia can be initiated during or before re-entry and surgical dissection; in the case of a catastrophic injury repair can be initiated undelayed.et al. [Shetty et al.  reportedet al.  also repThe results of the present study confirm and extend these previous observations. In our series, by comparing 32 recannulation cases with 100 primary cannulations, all within a reoperative proximal thoracic aortic surgery cohort, we could not identify any statistically significant differences in regard to multiple clinical outcomes like cannulation-related complications or postoperative complications. We have previously shown that cannulation of the RAA through a side-graft is superior to direct cannulation in regard to cannulation-related complications and stroke rates  and our Our data show that recannulation of the RAA in proximal thoracic aortic surgery is efficient and safe. All the advantages of this beneficial cannulation site are preserved—no flow reversal, the possibility of establishing selective ACP, hypothermia and CPB and in the specific case of redo surgery even expanded by the role of RAA recannulation as a preventive and rescue measure.This is a retrospective study with all the inherent limits and risks for confounding and bias. Our clinical database might under-report events and risk factors and we were not able to gather late clinical outcome data or data on quality of life. The operations were performed in a high-volume centre with experience in aortic surgery and the choice of cannulation site and cerebral protection strategy was surgeon’ preference. Also, in the recannulation group, a side-graft was more often used as in the primary cannulation group, fact that could also be responsible for the rate of complications observed , 14.A major limitation of the study is the limited number of cases. Furthermore, our results are not adjusted for major confounders. Larger multicentric trials should further clarify the safety of the procedure.Recannulation of the RAA in reoperative proximal thoracic aortic surgery is not associated with worse clinical outcomes and can be safely and effectively performed. In complex redo proximal thoracic aortic operations, all the reported advantages of RAA cannulation are extended by the role of preventive and rescue measure that this inflow site can play; previous cannulation of the axillary artery is not an impediment and should not hinder the decision for recannulation.Conflict of interest: The conflict of interest of Martin Czerny with Terumo, Medtronic and Cryolife does not have anything in common with materials used in the study or with the conclusions. All relevant data are within the manuscript and its supporting information files.Paul-Cătălin Puiu: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Software; Supervision; Validation; Visualization; Writing—original draft. Clarence Pingpoh: Conceptualization; Supervision; Validation; Visualization. Maximilian Kreibich: Conceptualization; Data curation; Investigation; Supervision; Validation. Martin Czerny: Conceptualization; Investigation; Methodology; Supervision; Validation. Emmanuel Zimmer: Conceptualization; Data curation; Supervision; Writing—review & editing. Friedhelm Beyersdorf: Conceptualization; Methodology; Project administration; Visualization; Writing—review & editing. Matthias Siepe: Conceptualization; Methodology; Project administration; Resources; Supervision; Writing—review & editing.Interactive CardioVascular and Thoracic Surgery thanks Mitsuru Asano and the other, anonymous reviewer(s) for their contribution to the peer review process of this article.


Miscarriage poses a significant threat to pregnant women globally. Recurrent miscarriages or potential poor embryonic development indicated by early drops in serum human chorionic gonadotrophin (hCG) are even more catastrophic for pregnant women. However, these patients receive either individualized medical intervention supported by limited evidence or no treatment at all. In this study, we report ten patients who shared at least one episode of an early decline of hCG in the first trimester and were treated with compassionate use of tumor necrosis factor-alpha inhibitor (TNFi). They were then followed up regularly with caution. Their hCG trajectory all resumed a normal pattern within one week and the obstetric outcomes were promising. No adverse fetal, neonatal, or maternal health issues have been observed. This case series supports current safety evidence of TNFi and provides new insight into its use in pregnancy when the embryo is in danger. Further well-designed clinical trials should be carried out to consolidate the evidence. Miscarriage is defined as the loss of pregnancy before viability. It causes significant psychological and economic damage to the family and society . AdoptinhCG is a glycoprotein with a molecular weight of 36,000 to 40,000 Dalton. It is almost exclusively produced by trophoblast cells and placenta after placental formation . An earlTumor necrosis factor-alpha (TNF-α) is an inflammatory cytokine belonging to Th1 lymphocytes. TNF-α blockers inhibit the effect of TNF-α. The agents were used in rheumatoid arthritis (RA), ankylosing spondylitis, inflammatory bowel disease (IBD), and psoriasis . It has In this case series, we report the trial of TNFi-led pregnancy-rescue management (TPRM) to help women with early signs of miscarriage. Most of the women in our study had a history of recurrent pregnancy loss according to the definition from the European Society of Human Reproduction and Embryology (ESHRE) guideline . Some caThe basic characteristics include women’s age, body mass index (BMI), previous obstetric background, and obstetric outcomes in current pregnancies. Details can be seen in The overall serial serum levels of hCG are shown in Three out of the ten cases were pregnancies following ART. Case No. 3 and 6 were diagnosed with unexplained infertility with at least two episodes of early embryonic failures after ART, while case No. 5 was affected by tubal factors. In case 3, upon the first episode, when hCG declined from 43,271 to 43,185, the patient had concurrent minor vaginal bleeds, indicating a threatened miscarriage. She received 1g tranexamic acid twice daily and a single dose of adalimumab (40mg) for one day and the bleeds stopped. A further decline on Day 55 prompted a second off-label use of adalimumab. Six days after injection, the serum hCG level went back on track . In contrast, case No.6 received only one dose of adalimumab despite having experienced two episodes of early declines of hCG. Case No. 5 had only experienced one episode of hCG early decline and received one single dose of adalimumab on that day. She is now 33wks and is followed up regularly in the antenatal clinic and no stillbirth or any other adverse fetal or maternal outcome has been reported.Among the seven patients who were conceptualized naturally, case No. 9 and 10 had two and four previous spontaneous miscarriages, respectively. Case No. 2 and 7 had a successful obstetric history before the current pregnancy. Case No. 8 was not complicated with pre-existing maternal medical conditions, nor did she have a previous history of miscarriage. However, she suffered from a long-lasting early decrease of hCG for more than ten days, suggesting an adverse embryonic outcome. A similar case was observed in No. 4, who is now 31wks, after being offered a single dose of certolizumab pegol (200mg) at 7wks when hCG fell from 58,016 to 54,438 mIU/mL the following day.Eight out of the ten women have delivered so far. The average birth weight was 3377.50g (2780-3850g), which fell into the normal reference range. Despite uneventful pregnancies after the rescue therapy, we identified a disproportionally high cesarean section rate in the study group. Case No. 8 is the only one so far who has delivered vaginally. Case No. 3 and 6 had cesarean for fetal distress in labor, possibly resulted from oligohydramnios (not an absolute indication for cesarean but could lead to increased emergency cesarean delivery in labor). Case No. 2 and 7 had CS (C-sections) indicated for previous CS delivery, a major obstetric indication in China. The other three cases had requested a cesarean delivery  in spite of any obstetric indications. This partly reflects China’s high rate of CS in the low obstetric risk population .In most cases, women would generally be offered expectant management if a spontaneous miscarriage is highly anticipated. However, the novel use of TNF-α blocker therapy has rendered promising obstetric outcomes in all patients. Safety concerns have also been addressed as no maternal-fetal or neonatal adverse consequences are reported in any patient. To our knowledge, this is the first report on the compassionate use of TNF-α blocker therapy to rescue endangered pregnancies.There are guidelines for pre-conceptional and prenatal care in China, but they aim to serve the general population . Anxiousvia ultrasound despite falling hCG levels.Highly individualized therapies are currently practiced in response to unexpected results revealed in early pregnancy, particularly in women with a history of multiple pregnancy losses but without definite explanatory causes for their recurrent pregnancy losses , 23. We The maternal immune system strikes an intricate balance between supporting embryonic development and maintaining immune responses in pregnancy. A dysregulated maternal immune system in pregnancy can affect obstetric outcomes , 25. TNFWe used adalimumab (Humira) and certolizumab pegol (Cimzia) in all cases. Adalimumab is a monoclonal IgG1 antibody, which is actively transported across the placenta by the neonatal Fc receptor (FcRn) . CertoliIn our case series, we proposed a different therapeutic strategy only when pregnancies were endangered. All cases were closely monitored with serial serum hCG tests before and after adalimumab or certolizumab injection. The trajectory of hCG resumed a normalized pattern day 4.9 on average. In contrast, it takes about two weeks for TNF-α blockers to relieve the symptoms in patients with diseases such as RA. Hence, we assume from the data that it takes much less time for the agents to modulate the microenvironment in the maternal-fetal interface .Compared with low-dose aspirin and LMWH or progesterone used in other clinical trials to prevent miscarriages in recent years, we use the TNFi as the primary therapy for rescuing pregnancies instead of preventing miscarriages , 23, 29.With this trial, we are the first to report a novel strategy with the use of TNFi that could save pregnancies with potential risks of miscarriage. It offers insights to other healthcare providers when they have patients with reduced hCG in early pregnancy. This may benefit pregnancy in the specific situation without compromising maternal and neonatal health. However, there are some limitations to our study. First, the number of this series is too small to reach a consolidated conclusion. Second, all women have been offered a range of medications rather than TNFi-only therapy. The use of medicines could compromise the pharmacological effect of TNF-α blockers in rescuing pregnancy. Third, although all early declines of hCG were observed in the same hospital, human factors could add to inaccurate testing of serum hCG levels in early pregnancy. However, even if we consider this, a sub-optimal growth is still considered to indicate possibly poor embryonic development.Our study has demonstrated that TNFi can reverse potentially poor pregnancy outcomes in the first trimester. However, it can only be used in the context of a clinical research setting, and further trials and experiments should be carried out to confirm the pharmacological effect of TNFi in rescuing pregnancies at risk.Case No. 1: I agree to the novel use of TNFi-led therapy for my specific situation and to report any problems for a better medical understanding and improvement of treatment options for women with a potential risk of miscarriage. I hope this enables the doctors to help pregnant women with safe and effective treatment. I am now very happy with my newborn baby.Case No. 2: I agree to report on my case, the pregnancy, and the data of my baby. I hope this article can help other women like me get appropriate therapy for their threatened miscarriages.Case No. 4: I agree to report my particular condition for a better medical understanding and improvement of treatment options for pregnant women whose hCG declines in early pregnancy. At present, I am 31 weeks pregnant. The antenatal examination and investigations have been normal and the fetus is developing well. I hope that this report could help more patients who share similar conditions with me in China and other countries worldwide.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of Zhejiang Provincial People’s Hospital. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.ZZ and FZ developed the idea for this study. ZZ wrote the manuscript and FZ reviewed it. YW and GW are involved in the diagnostic and therapeutic care and follow-up of the patients. YW and ZZ are also responsible for the tables and figures formation and revision. All authors contributed to the article and approved the submitted version.This study is co-supported by Zhejiang Provincial Project for Medical and Health Science and Technology (Grant no. 2022503241) and Zhejiang Provincial Project for Medical Education (Grant no. Y202146113).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.